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Microsoft.Extensions.AI Integration

Cross-SDK comparison

See the centralized MEAI documentation for feature matrices and comparisons across all tryAGI SDKs.

The Jina SDK implements IEmbeddingGenerator<string, Embedding<float>> from Microsoft.Extensions.AI, providing a unified interface for generating text embeddings.

Installation

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dotnet add package tryAGI.Jina

Generate Embeddings

JinaClient directly implements IEmbeddingGenerator<string, Embedding<float>>, so you can use it with the standard MEAI interface.

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using Microsoft.Extensions.AI;
using Jina;

IEmbeddingGenerator<string, Embedding<float>> generator =
    new JinaClient(apiKey: Environment.GetEnvironmentVariable("JINA_API_KEY")!);

var embeddings = await generator.GenerateAsync(["Hello, world!"]);

Console.WriteLine($"Dimensions: {embeddings[0].Vector.Length}");
Console.WriteLine($"Model: {embeddings[0].ModelId}");

Batch Embeddings

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using Microsoft.Extensions.AI;
using Jina;

IEmbeddingGenerator<string, Embedding<float>> generator =
    new JinaClient(apiKey: Environment.GetEnvironmentVariable("JINA_API_KEY")!);

var texts = new[]
{
    "The quick brown fox jumps over the lazy dog.",
    "Machine learning is a subset of artificial intelligence.",
    "Embeddings represent text as numerical vectors.",
};

var embeddings = await generator.GenerateAsync(texts);

Console.WriteLine($"Generated {embeddings.Count} embeddings");
Console.WriteLine($"Input tokens: {embeddings.Usage?.InputTokenCount}");

Custom Dimensions

Jina embeddings v3 supports custom output dimensions via EmbeddingGenerationOptions.Dimensions:

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using Microsoft.Extensions.AI;
using Jina;

IEmbeddingGenerator<string, Embedding<float>> generator =
    new JinaClient(apiKey: Environment.GetEnvironmentVariable("JINA_API_KEY")!);

var options = new EmbeddingGenerationOptions
{
    ModelId = "jina-embeddings-v3",
    Dimensions = 256, // Reduce from default 1024
};

var embeddings = await generator.GenerateAsync(
    ["Compact embedding for efficient storage."], options);

Console.WriteLine($"Dimensions: {embeddings[0].Vector.Length}"); // 256

Semantic Similarity

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using Microsoft.Extensions.AI;
using Jina;

IEmbeddingGenerator<string, Embedding<float>> generator =
    new JinaClient(apiKey: Environment.GetEnvironmentVariable("JINA_API_KEY")!);

var embeddings = await generator.GenerateAsync([
    "How do I reset my password?",
    "I forgot my login credentials.",
    "What are your business hours?",
]);

// Compute cosine similarity
static float CosineSimilarity(ReadOnlySpan<float> a, ReadOnlySpan<float> b)
{
    float dot = 0, normA = 0, normB = 0;
    for (int i = 0; i < a.Length; i++)
    {
        dot += a[i] * b[i];
        normA += a[i] * a[i];
        normB += b[i] * b[i];
    }
    return dot / (MathF.Sqrt(normA) * MathF.Sqrt(normB));
}

var sim01 = CosineSimilarity(embeddings[0].Vector.Span, embeddings[1].Vector.Span);
var sim02 = CosineSimilarity(embeddings[0].Vector.Span, embeddings[2].Vector.Span);

Console.WriteLine($"'password reset' vs 'forgot credentials': {sim01:F4}");
Console.WriteLine($"'password reset' vs 'business hours': {sim02:F4}");

Dependency Injection

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using Microsoft.Extensions.AI;
using Jina;

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddSingleton<IEmbeddingGenerator<string, Embedding<float>>>(
    new JinaClient(apiKey: builder.Configuration["Jina:ApiKey"]!));

Provider Metadata

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var metadata = generator.GetService<EmbeddingGeneratorMetadata>();
Console.WriteLine($"Provider: {metadata?.ProviderName}"); // "JinaClient"
Console.WriteLine($"Endpoint: {metadata?.ProviderUri}");